We use two different approaches for Bayesian D-optimal sensor placement: the greedy technique of [1]} and the convex relaxation approach by [2]}.
In the greedy approach, we leverage the submodularity of the objective in the case when \({\mathbf {T}}={\mathbf {I}}\)  in order to use the accelerated greedy algorithm of M. Minoux [3]}.
For the convex approach, we wrote a direct Python translation of a MATLAB code
written by S. Joshi and S. Boyd that implements a Newton method with line
search, and may be found at https://web.stanford.edu/~boyd/papers/matlab/sensor_selection/.
We use the gradient and Hessian matrices for the Bayesian D-optimal objective from their paper [2]}.
